Ship AI features your users actually adopt — in 6-8 weeks.

Not a prototype. Not a slide deck. Production AI, built on your user data, with adoption guarantees.

Book AI Strategy Call See How It Works
10%+
adoption guaranteed
6-8 weeks
to production
3-5
features per engagement

Your board keeps asking about AI strategy. You don't have one.

Your engineering team is researching AI. Nothing has shipped in 6 months.

They're evaluating models, testing APIs, building prototypes in staging. But nothing is live. Nothing is generating revenue.

Competitors announced AI features. Yours are still "in development".

Every week you wait, the gap widens. They're closing deals on AI roadmaps. You're still in "evaluation mode".

You don't know which AI features would actually move revenue.

Everyone has opinions. Nobody has data. You're guessing which AI use cases your users would actually adopt.

6 months from now: 3-5 AI features that users actually adopt.

Your board sees AI adoption metrics in every meeting. Your sales team closes deals faster because you have AI capabilities competitors don't.

Your engineering team has a repeatable playbook for shipping AI features. No more "we need to research this." No more 6-month "evaluations." They ship AI the way they ship any other feature — with confidence, with instrumentation, with adoption targets.

You're not asking "should we do AI?" You're asking "which AI feature next?"

What changes after 6 weeks

Before After
AI strategy is a slide deck AI strategy is 3-5 shipped features with adoption data
Prototypes dying in staging Production features with 10%+ adoption
Engineering researching "which model to use" Engineering shipping features on a repeatable playbook
Board asks "what's our AI strategy?" Board sees AI adoption, retention lift, expansion revenue
Competitors announce AI first You're known as "the AI company" in your category

How It Works

Two frameworks. One outcome: AI that ships.

A.I.M. runs the engagement. D.A.T.A. ensures your foundation is ready. Together: production AI in 6-8 weeks.

A
Phase 1

Assess

Week 1-2. We identify which AI features would actually move revenue — not which models are coolest.

  • AI opportunity map (5-8 use cases ranked)
  • Build vs. buy recommendation
  • Revenue potential for each use case
  • D.A.T.A. readiness assessment
I
Phase 2

Implement

Week 3-6. We build and deploy one production AI feature — not a prototype, not a proof of concept.

  • Production AI feature (deployed)
  • Model selection + fine-tuning
  • AI UX patterns (onboarding, explainability)
  • Adoption instrumentation
M
Phase 3

Measure

Week 7-8. We track adoption, iterate based on usage data, and hand off a playbook your team can use.

  • Success dashboard (real-time adoption)
  • Iteration backlog (based on 30-day usage)
  • AI playbook (your team's internal guide)
  • 30-day adoption report

Your AI is only as good as your data.

Before we build anything, we run D.A.T.A. — a readiness assessment that tells you if your data can actually support AI. No surprises in week 4.

D

Depth

Do you have enough historical data for model training?

  • Volume assessment by use case
  • Time range adequacy check
  • Minimum viable dataset identified
A

Accuracy

Is your data clean, labeled, and reliable?

  • Data quality scoring
  • Label consistency audit
  • Missing value analysis
T

Taxonomy

Is your event tracking structured for ML consumption?

  • Event schema review
  • Feature engineering readiness
  • Real-time vs. batch assessment
A

Access

Can models query your data in real-time, or is it siloed?

  • Data pipeline audit
  • API accessibility check
  • Integration complexity score

Why this matters: We've seen teams spend $200K on AI features only to discover their data couldn't support them. D.A.T.A. catches this in week 1. If your data scores low, we fix that first — before writing a single model training script.

Pricing

Three ways to engage

AI Opportunity Audit

One-time · 3 weeks

$7,997
  • 5-8 AI use cases ranked by revenue impact
  • Data readiness assessment
  • Competitive AI feature analysis
  • Build vs. buy recommendation
  • Implementation roadmap
Book Strategy Call

AI Feature Build Sprint

One-time · 6-8 weeks

$35K–$65K
  • 1 production AI feature shipped
  • Model fine-tuning or API integration
  • AI UX patterns + onboarding
  • Adoption instrumentation
  • Success dashboard + iteration plan
Book Strategy Call

AI Growth OS

Ongoing · 3-month minimum

$18K–$28K/mo
  • 1-2 AI features shipped per month
  • Adoption dashboard (updated weekly)
  • Model performance monitoring
  • Competitive AI feature tracking
  • Monthly AI strategy review
Book Strategy Call

What's included (AI Feature Sprint — $50K example)

AI Opportunity Assessment $15,000
Data Pipeline Build $12,000
Model Development $18,000
AI UX Design $10,000
Production Deployment $8,000
Adoption Instrumentation $7,000
Total itemized value $70,000
AI Feature Sprint price $50,000

Adoption Guarantee

If your AI feature doesn't hit 10% adoption among active users within 60 days of launch, we iterate free until it does. We're incentivized to build something your users actually want — not just something that works technically.

Common questions

Everything you need to know before booking a call.

What if our data isn't ready for AI? +
We run a D.A.T.A. assessment in week 1 — Depth, Accuracy, Taxonomy, Access. If your data scores low, we fix that first. Sometimes that means 2 weeks of data pipeline work before model training. Better to fix data upfront than build a model on garbage.
Do we need ML infrastructure already set up? +
No. We use whatever gets the job done fastest. Sometimes that's Hugging Face + FastAPI. Sometimes it's your existing cloud infrastructure. We're tool-agnostic — we optimize for speed to production, not technical purity.
What happens after the sprint? +
You have a production AI feature with adoption tracking. Most clients continue into AI Growth OS ($18K-$28K/mo) to ship 1-2 more features per month. Some take the playbook and run it themselves. Both are fine — you own everything we build.
Can you work with our existing stack? +
Yes. We've deployed on AWS, GCP, Azure, Vercel, and everything in between. We integrate with your existing auth, databases, and APIs. The goal is to ship AI that feels native to your product — not a bolted-on demo.
How is this different from an AI consultancy? +
Most AI consultancies deliver a prototype and leave. We deliver production features with adoption guarantees. They measure accuracy. We measure adoption + revenue. They hand you a Jupyter notebook. We hand you shipped code with instrumentation.

Ready to ship your first AI feature?

6-8 weeks. Production-ready. 10% adoption guaranteed.

Book AI Strategy Call See Sample AI Roadmap